Van-Khoa Le

Elias Munoz, Pierre Baudot, Van-Khoa Le et al.

Pathologies systematically induce morphological changes, thus providing a major but yet insufficiently quantified source of observables for diagnosis. The study develops a predictive model of the pathological states based on morphological features (3D-morphomics) on Computed Tomography (CT) volumes. A complete workflow for mesh extraction and simplification of an organ's surface is developed, and coupled with an automatic extraction of morphological features given by the distribution of mean curvature and mesh energy. An XGBoost supervised classifier is then trained and tested on the 3D-morphomics to predict the pathological states. This framework is applied to the prediction of the malignancy of lung's nodules. On a subset of NLST database with malignancy confirmed biopsy, using 3D-morphomics only, the classification model of lung nodules into malignant vs. benign achieves 0.964 of AUC. Three other sets of classical features are trained and tested, (1) clinical relevant features gives an AUC of 0.58, (2) 111 radiomics gives an AUC of 0.976, (3) radiologist ground truth (GT) containing the nodule size, attenuation and spiculation qualitative annotations gives an AUC of 0.979. We also test the Brock model and obtain an AUC of 0.826. Combining 3D-morphomics and radiomics features achieves state-of-the-art results with an AUC of 0.978 where the 3D-morphomics have some of the highest predictive powers. As a validation on a public independent cohort, models are applied to the LIDC dataset, the 3D-morphomics achieves an AUC of 0.906 and the 3D-morphomics+radiomics achieves an AUC of 0.958, which ranks second in the challenge among deep models. It establishes the curvature distributions as efficient features for predicting lung nodule malignancy and a new method that can be applied directly to arbitrary computer aided diagnosis task.

Marco Bertolini, Van-Khoa Le, Jake Pencharz et al.

In pre-clinical pathology, there is a paradox between the abundance of raw data (whole slide images from many organs of many individual animals) and the lack of pixel-level slide annotations done by pathologists. Due to time constraints and requirements from regulatory authorities, diagnoses are instead stored as slide labels. Weakly supervised training is designed to take advantage of those data, and the trained models can be used by pathologists to rank slides by their probability of containing a given lesion of interest. In this work, we propose a novel contextualized eXplainable AI (XAI) framework and its application to deep learning models trained on Whole Slide Images (WSIs) in Digital Pathology. Specifically, we apply our methods to a multi-instance-learning (MIL) model, which is trained solely on slide-level labels, without the need for pixel-level annotations. We validate quantitatively our methods by quantifying the agreements of our explanations' heatmaps with pathologists' annotations, as well as with predictions from a segmentation model trained on such annotations. We demonstrate the stability of the explanations with respect to input shifts, and the fidelity with respect to increased model performance. We quantitatively evaluate the correlation between available pixel-wise annotations and explainability heatmaps. We show that the explanations on important tiles of the whole slide correlate with tissue changes between healthy regions and lesions, but do not exactly behave like a human annotator. This result is coherent with the model training strategy.

Sylvain Bodard, Pierre Baudot, Benjamin Renoust et al.

Early detection of malignant lung nodules remains limited by reliance on size- and growth-based screening criteria, which can delay diagnosis. We present an integrated AI system that - unlike conventional CADe or CADx approaches - jointly performs nodule detection and malignancy assessment directly at the nodule level from low-dose CT scans within a unified aided decision framework. To address limitations in dataset scale and explainability, we designed an ensemble of shallow deep learning and feature-based specialized models, trained and evaluated on 25,709 scans with 69,449 annotated nodules, with external validation on an independent cohort. The system achieves an area under the receiver operating characteristic curve (AUC) of 0.98 internally and 0.945 on an independent cohort, outperforming radiologists and leading AI models (Sybil, Brock, Google, Kaggle). With a sensitivity of 99.3 percent at 0.5 false positives per scan, it addresses key barriers to AI adoption and demonstrates improved performance relative to both Lung-RADS size-based triage and European volume- and VDT-based screening criteria. The model outperforms radiologists across all nodule sizes and cancer stages - excelling in stage I cancers - and across all growth-based metrics, including volume-doubling time. It also surpasses radiologists by up to one year in diagnosing indeterminate and slow-growing nodules.